Security Scan Report: africangrovehugo-hrtt52jxs0.edgeone.app

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Submitted: Completed: publiccompleted
This website contacted 7 IPs in 2 countries across 12 domains to perform 21 HTTP transactions. The main domain is edgeone.app and was registered 3 years 5 months ago.

Submitted URL: https://africangrovehugo-hrtt52jxs0.edgeone.app/17188-one-championship-angela-lee-reveals-2017-car-crash-was-suicide-attempt-confirms-sister-victoria-to.html

The Cisco Umbrella rank of the primary domain is #455,732 of the top 1 million websites

AI Security Verdict

Moderate Risk

Confidence: 78%

5
Risk Score

The site is a news‑style article on a newly created subdomain with low reputation; no direct malicious activity detected but caution is advised.

Risk Factors (5)
Unknown subdomain age on hosting platform
Low domain reputation ranking
High JavaScript obfuscation (potentially suspicious code)
Brand mention on a low‑reputation, unrelated domain
Hosting on a generic sub‑domain service
Safety Factors (5)
Page type is an article (og:type="article"), indicating news content
No credential or payment forms present
No Indicators of Compromise matched in threat intelligence
No JavaScript malware patterns detected by YARA
No network IDS alerts
Domain age information unavailable

Details

Page Title

ONE Championship: Angela Lee reveals 2017 car crash was suicide attempt, confirms sister Victoria to

Scan Type

public

Domain Name Analysis

Within the application-focused generic top-level domain (.app), 'africangrovehugo-hrtt52jxs0.edgeone.app' is registered; it also runs on subdomain 'africangrovehugo-hrtt52jxs0'. The registrable portion 'edgeone' spans 7 characters containing four vowels alongside three consonants. It segments into 2 words: edge, one. Median word length is 3.5 characters. No strong language cues emerged from the frequency lists.

Screenshot

Security scan screenshot of https://africangrovehugo-hrtt52jxs0.edgeone.app/17188-one-championship-angela-lee-reveals-2017-car-crash-was-suicide-attempt-confirms-sister-victoria-to.html

Page Load Overview

21.12s
Total Load Time
174 KB
Total Size

Language Analysis

Primary Language

🇺🇸English
Code: en
Confidence:80%
Script:Latin
Direction:ltr

Detection Details

HTML Lang Attribute:en-US
Text Length:5,213 chars
Detector Agreement:100%

Website Classification

Primary Category

corporate35% confidence
Type: dynamic
Method: ml+structural

All Detected Categories

corporate
35%
healthcare medical
32%
news media journalism
27%

Detected Features

OG: article
Schema.org

Domain & IP Information

RequestsIP AddressLocationAS Autonomous System
3142.251.110.94United States
AS15169Google LLC
3172.240.108.84United States
AS7979Servers.com, Inc.
3142.251.20.95United States
AS15169Google LLC
3101.33.10.57Frankfurt am Main, Hesse, Germany
3172.66.169.241United States
AS13335Cloudflare, Inc.
3172.67.150.240United States
AS13335Cloudflare, Inc.
3104.20.7.223United States
AS13335Cloudflare, Inc.
217--

Detected Technologies3

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T14E723B13F284062CF7215650F98276BEB96DC03AD3538D90B9DD671CA7CA8EA89317CD

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

384:ZGWhnYf8zX+IGJKJXJ/cDo8KHrJB9F/mgG1fnYYrWag4MDw:YWhnz+IDJUDMLJp/mX1fYYrWag4Aw

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:17502:kMESqlQ8CIjIII0gUGZOTs7SgBcpYLYvIIRDcmjgBJACISDAHQEORB6EIhJ30zh4Qc4guAJCAAMBAUSpKIgwkUFeAbBMnQ6N

These hashes enable detection of similar websites and malware variants by comparing content similarity even when exact matches aren't found.

Image Hashes

Perceptual Hashes

Average Hash:839fff83c3c383ff
Perceptual Hash:bd1d92387161c3d3
Difference Hash:163a1a063a272f1a
Wavelet Hash:829fc383838383ff
Color Hash:#aca953

Other Hashes

Crop Resistant:163a1a063a272f1a

Scan History

Scan history not available

Unable to load historical scan data